Bioinformatic analysis predicts that ethanol exposure during early development causes alternative splicing alterations of genes involved in RNA post-transcriptional regulation.

Prenatal ethanol exposure is associated with neurodevelopmental defects and long-lasting cognitive deficits, which are grouped as fetal alcohol spectrum disorders (FASD). The molecular mechanisms underlying FASD are incompletely characterized. Alternative splicing, including the insertion of microexons (exons of less than 30 nucleotides in length), is highly prevalent in the nervous system. However, whether ethanol exposure can have acute or chronic deleterious effects in this process is poorly understood. In this work, we used the bioinformatic tools VAST-TOOLS, rMATS, MAJIQ, and MicroExonator to predict alternative splicing events affected by ethanol from available RNA sequencing data. Experimental protocols of ethanol exposure included human cortical tissue development, human embryoid body differentiation, and mouse development. We found common genes with predicted differential alternative splicing using distinct bioinformatic tools in different experimental designs. Notably, Gene Ontology and KEGG analysis revealed that the alternative splicing of genes related to RNA processing and protein synthesis was commonly affected in the different ethanol exposure schemes. In addition, the inclusion of microexons was also affected by ethanol. This bioinformatic analysis provides a reliable list of candidate genes whose splicing is affected by ethanol during nervous system development. Furthermore, our results suggest that ethanol particularly modifies the alternative splicing of genes related to post-transcriptional regulation, which probably affects neuronal proteome complexity and brain function.

Hidden Markov Modeling with HMMTeacher.

Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user's manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.